{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf548de4-86b8-403b-85e7-7da5de23896b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "客户信息数据集\n",
      "    age  height  weight gender\n",
      "0    21     163      60      M\n",
      "1    22     164      56      M\n",
      "2    21     170      50      M\n",
      "3    23     168      56      M\n",
      "4    21     169      60      M\n",
      "..  ...     ...     ...    ...\n",
      "95   24     192      73      F\n",
      "96   25     187      74      F\n",
      "97   20     178      65      F\n",
      "98   23     172      76      F\n",
      "99   25     173      78      F\n",
      "\n",
      "[100 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "names=['age','height','weight','gender']\n",
    "dataset=pd.read_csv('gender-data-y.txt',delimiter=',',names=names)\n",
    "print('客户信息数据集')\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c53ae29e-4589-4157-9f59-858965894cba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "处理后的客户信息数据集\n",
      "    age  height  weight gender  latex\n",
      "0    21   163.0    60.0      M      1\n",
      "1    22   164.0    56.0      M      1\n",
      "2    21   170.0    50.0      M      1\n",
      "3    23   168.0    56.0      M      1\n",
      "4    21   169.0    60.0      M      1\n",
      "..  ...     ...     ...    ...    ...\n",
      "95   24   192.0    73.0      F      0\n",
      "96   25   187.0    74.0      F      0\n",
      "97   20   178.0    65.0      F      0\n",
      "98   23   172.0    76.0      F      0\n",
      "99   25   173.0    78.0      F      0\n",
      "\n",
      "[100 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "dataset['height']=dataset['height'].astype(float)\n",
    "dataset['weight']=dataset['weight'].astype(float)\n",
    "le=preprocessing.LabelEncoder()\n",
    "dataset['latex']=le.fit_transform(dataset['gender'])\n",
    "print('处理后的客户信息数据集')\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09a5cd4b-7b3d-4f83-93f5-b873e712fe56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "data=dataset.iloc[range(0,100),range(1,3)].values\n",
    "target=dataset.iloc[range(0,100),range(4,5)].values.reshape(1,100)[0]\n",
    "plt.scatter(data[target==0,0],data[target==0,1],s=60,c='r',marker='o')\n",
    "plt.scatter(data[target==1,0],data[target==1,1],s=60,c='g',marker='^')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.xlabel('身高/cm')\n",
    "plt.ylabel('体重/kg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "29c7fc98-cdaa-49f7-a053-f72d601eba8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "x,y=data,target\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=30,random_state=0)\n",
    "depth=np.arange(1,15)\n",
    "err_list=[]\n",
    "for i in depth:\n",
    "    model=DecisionTreeClassifier(criterion='entropy',max_depth=i)\n",
    "    model.fit(x_train,y_train)\n",
    "    pred=model.predict(x_test)\n",
    "    ac=accuracy_score(y_test,pred)\n",
    "    err=1-ac\n",
    "    err_list.append(err)\n",
    "plt.plot(depth,err_list,'ro-')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.xlabel('决策树深度')\n",
    "plt.ylabel('预测误差率')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97c418f4-3f6f-416f-8c23-99a355a23afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "model=DecisionTreeClassifier(criterion='entropy',max_depth=5)\n",
    "model.fit(x_train,y_train)\n",
    "pred=model.predict(x_test)\n",
    "re\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
